
"""simple docstring"""


from math import factorial



class 	__a	:






       def __init__(	self			:				int							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				Optional[int]      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		real
              if isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ):
                     UpperCamelCase       			=		[1] * rank
              else:
                     UpperCamelCase       			=		rank






       def __repr__(	self			:				int      )->	Dict:







              """simple docstring"""
              return (
                  f"{self.real}+"
                  f"{'+'.join(str(UpperCAmelCase_      )+'E'+str(n+1      )for n,dual in enumerate(self.duals      )      )}"
              )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		self.duals.copy()
              while cur[-1] == 0:
                     cur.pop(-1      )
              return Dual(self.real							,   UpperCAmelCase_      )






       def __add__(	self			:				Any							,   UpperCAmelCase_			:				Optional[Any]      )->	Tuple:







              """simple docstring"""
              if not isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ):
                     return Dual(self.real + other							,   self.duals      )
              UpperCamelCase       			=		self.duals.copy()
              UpperCamelCase       			=		other.duals.copy()
              if len(UpperCAmelCase_      ) > len(UpperCAmelCase_      ):
                     o_dual.extend([1] * (len(UpperCAmelCase_      ) - len(UpperCAmelCase_      ))      )
              elif len(UpperCAmelCase_      ) < len(UpperCAmelCase_      ):
                     s_dual.extend([1] * (len(UpperCAmelCase_      ) - len(UpperCAmelCase_      ))      )
              UpperCamelCase       			=		[]
              for i in range(len(UpperCAmelCase_      )      ):
                     new_duals.append(s_dual[i] + o_dual[i]      )
              return Dual(self.real + other.real							,   UpperCAmelCase_      )

       UpperCamelCase_		:       Any				=      __add__






       def __sub__(	self			:				str							,   UpperCAmelCase_			:				List[Any]      )->	List[Any]:







              """simple docstring"""
              return self + other * -1






       def __mul__(	self			:				List[Any]							,   UpperCAmelCase_			:				str      )->	Optional[int]:







              """simple docstring"""
              if not isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ):
                     UpperCamelCase       			=		[]
                     for i in self.duals:
                            new_duals.append(i * other      )
                     return Dual(self.real * other							,   UpperCAmelCase_      )
              UpperCamelCase       			=		[0] * (len(self.duals      ) + len(other.duals      ) + 1)
              for i, item in enumerate(self.duals      ):
                     for j, jtem in enumerate(other.duals      ):
                            new_duals[i + j + 1] += item * jtem
              for k in range(len(self.duals      )      ):
                     new_duals[k] += self.duals[k] * other.real
              for index in range(len(other.duals      )      ):
                     new_duals[index] += other.duals[index] * self.real
              return Dual(self.real * other.real							,   UpperCAmelCase_      )

       UpperCamelCase_		:       Tuple				=      __mul__






       def __truediv__(	self			:				List[Any]							,   UpperCAmelCase_			:				Union[str, Any]      )->	List[str]:







              """simple docstring"""
              if not isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ):
                     UpperCamelCase       			=		[]
                     for i in self.duals:
                            new_duals.append(i / other      )
                     return Dual(self.real / other							,   UpperCAmelCase_      )
              raise ValueError






       def __floordiv__(	self			:				List[Any]							,   UpperCAmelCase_			:				List[str]      )->	List[str]:







              """simple docstring"""
              if not isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ):
                     UpperCamelCase       			=		[]
                     for i in self.duals:
                            new_duals.append(i // other      )
                     return Dual(self.real // other							,   UpperCAmelCase_      )
              raise ValueError






       def __pow__(	self			:				Tuple							,   UpperCAmelCase_			:				Optional[int]      )->	Optional[Any]:







              """simple docstring"""
              if n < 0 or isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ):
                     raise ValueError("power must be a positive integer"      )
              if n == 0:
                     return 1
              if n == 1:
                     return self
              UpperCamelCase       			=		self
              for _ in range(n - 1      ):
                     x *= self
              return x





def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				str:







       """simple docstring"""
       if not callable(UpperCAmelCase_					):
              raise ValueError("differentiate() requires a function as input for func"					)
       if not isinstance(UpperCAmelCase_		,   (float, int)					):
              raise ValueError("differentiate() requires a float as input for position"					)
       if not isinstance(UpperCAmelCase_		,   UpperCAmelCase_					):
              raise ValueError("differentiate() requires an int as input for order"					)
       UpperCamelCase       			=		Dual(UpperCAmelCase_		,   1					)
       UpperCamelCase       			=		func(UpperCAmelCase_					)
       if order == 0:
              return result.real
       return result.duals[order - 1] * factorial(UpperCAmelCase_					)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



      def 						lowerCamelCase__      (				UpperCAmelCase_					)->				str:







             """simple docstring"""
             return y**2 * y**4

      print(differentiate(f, 9, 2))







"""simple docstring"""


import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Any				=      VQModel
       UpperCamelCase_		:       Dict				=      '''sample'''






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				List[str]=(32, 32)      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		4
              UpperCamelCase       			=		3

              UpperCamelCase       			=		floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCAmelCase_      )

              return {"sample": image}






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              return (3, 32, 32)






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Tuple:







              """simple docstring"""
              return (3, 32, 32)






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		{
                  "block_out_channels": [32, 64],
                  "in_channels": 3,
                  "out_channels": 3,
                  "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
                  "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
                  "latent_channels": 3,
              }
              UpperCamelCase       			=		self.dummy_input
              return init_dict, inputs_dict






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	List[Any]:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	str:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase       			=		VQModel.from_pretrained("fusing/vqgan-dummy"							,   output_loading_info=UpperCAmelCase_      )
              self.assertIsNotNone(UpperCAmelCase_      )
              self.assertEqual(len(loading_info["missing_keys"]      )							,   0      )

              model.to(UpperCAmelCase_      )
              UpperCamelCase       			=		model(**self.dummy_input      )

              assert image is not None, "Make sure output is not None"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		VQModel.from_pretrained("fusing/vqgan-dummy"      )
              model.to(UpperCAmelCase_      ).eval()

              torch.manual_seed(0      )
              if torch.cuda.is_available():
                     torch.cuda.manual_seed_all(0      )

              UpperCamelCase       			=		torch.randn(1							,   model.config.in_channels							,   model.config.sample_size							,   model.config.sample_size      )
              UpperCamelCase       			=		image.to(UpperCAmelCase_      )
              with torch.no_grad():
                     UpperCamelCase       			=		model(UpperCAmelCase_      ).sample

              UpperCamelCase       			=		output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              UpperCamelCase       			=		torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]      )
              # fmt: on
              self.assertTrue(torch.allclose(UpperCAmelCase_							,   UpperCAmelCase_							,   atol=1e-3      )      )





